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Beyond the barriers: family medicine residents’ attitudes towards providing Aboriginal health care

2011· article· en· W1959907625 on OpenAlexaffabout
Bonnie Larson, Leonie Herx, Tyler Williamson, Lynden Crowshoe

Bibliographic record

VenueMedical Education · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHealth careFamily medicineMedicineWork (physics)PopulationNursingEnvironmental health

Abstract

fetched live from OpenAlex

CONTEXT: Health care is one of many under-resourced areas in Aboriginal communities in Canada. Aboriginal people have substandard health compared with the general population, yet have less access to health care services. Not only is there a paucity of Aboriginal doctors, but it also appears that few non-Aboriginal doctors are willing or able to work in Aboriginal contexts. OBJECTIVES: This study examines the attitudes of family medicine residents towards providing health care to Aboriginal patients. The goal of this study was to assess the willingness of family medicine residents to work in Aboriginal health care and to elucidate the major factors that inform these attitudes. METHODS: We conducted a cross-sectional survey of an urban cohort of family medicine residents using a convenience sample. Our survey instrument consisted of a questionnaire comprising a mixture of open-ended and closed questions. RESULTS: Although a majority (52%, n = 27) of the family medicine residents were willing to work in Aboriginal contexts, many felt underprepared to do so (40%, n = 21). Residents who have had some exposure to Aboriginal issues and have had community experiences are more likely to state an intention to work in Aboriginal settings. CONCLUSIONS: The results of this study encourage the creation of educational experiences for medical residents that may promote a desire to work in Aboriginal communities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.811
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.390
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2011
Admission routes2
Has abstractyes

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